Techniques for behavioral pairing model evaluation in a contact center system

ABSTRACT

Techniques for behavioral pairing model evaluation in a contact center system are disclosed. In one particular embodiment, the techniques may be realized as a method for behavioral pairing model evaluation in a contact center system comprising determining an ordering of a plurality of agents, determining an ordering of a plurality of contact types; analyzing, historical contact-agent outcome data according to the orderings of the pluralities of agents and contact types to construct a pairing model; and determining an expected performance of the contact center system using the pairing model.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.15/377,397, filed Dec. 13, 2016, which is hereby incorporated byreference in its entirety as if fully set forth herein.

FIELD OF THE DISCLOSURE

This disclosure generally relates to model evaluation for pairingcontacts and agents in contact centers and, more particularly, totechniques for behavioral pairing model evaluation in a contact centersystem.

BACKGROUND OF THE DISCLOSURE

A typical contact center algorithmically assigns contacts arriving atthe contact center to agents available to handle those contacts. Attimes, the contact center may have agents available and waiting forassignment to inbound or outbound contacts (e.g., telephone calls,Internet chat sessions, email). At other times, the contact center mayhave contacts waiting in one or more queues for an agent to becomeavailable for assignment.

In some typical contact centers, contacts are assigned to agents orderedbased on time of arrival, and agents receive contacts ordered based onthe time when those agents became available. This strategy may bereferred to as a “first-in, first-out”, “FIFO”, or “round-robin”strategy. In other typical contact centers, other strategies may beused, such as “performance-based routing”, or a “PBR” strategy.

In other, more advanced contact centers, contacts are paired with agentsusing a “behavioral pairing”, or a “BP” strategy, under which contactsand agents may be deliberately (preferentially) paired in a fashion thatenables the assignment of subsequent contact-agent pairs such that whenthe benefits of all the assignments under a BP strategy are totaled theymay exceed those of FIFO and other strategies such as performance-basedrouting (“PBR”) strategies. BP is designed to encourage balancedutilization of agents within a skill queue while neverthelesssimultaneously improving overall contact center performance beyond whatFIFO or PBR methods will allow. This is a remarkable achievementinasmuch as BP acts on the same calls and same agents as FIFO or PBRmethods, utilizes agents approximately evenly as FIFO provides, and yetimproves overall contact center performance. BP is described in, e.g.,U.S. Pat. No. 9,300,802, which is incorporated by reference herein.Additional information about these and other features regarding thepairing or matching modules (sometimes also referred to as “SATMAP”,“routing system”, “routing engine”, etc.) is described in, for example,U.S. Pat. No. 8,879,715, which is incorporated herein by reference.

A BP strategy may develop a model of agents, or agent groups and contacttypes, from which expected gains over other pairing strategies may bedetermined. However, there are currently no techniques for improvingmodel generation and validation to optimize expected gains.

In view of the foregoing, it may be understood that there is a need fora system that enables improving behavioral pairing model selection toimprove the efficiency and performance of pairing strategies that aredesigned to choose among multiple possible pairings.

SUMMARY OF THE DISCLOSURE

Techniques for behavioral pairing model evaluation in a contact centersystem are disclosed. In one particular embodiment, the techniques maybe realized as a method for behavioral pairing model evaluation in acontact center system comprising determining an ordering of a pluralityof agents, determining an ordering of a plurality of contact types;analyzing, historical contact-agent outcome data according to theorderings of the pluralities of agents and contact types to construct apairing model; and determining an expected performance of the contactcenter system using the pairing model.

In accordance with other aspects of this particular embodiment, abehavioral pairing correction factor may be applied to the pairing modelprior to determining the expected performance.

In accordance with other aspects of this particular embodiment, thepairing model may be a behavioral pairing model and/or based on adiagonal pairing strategy.

In accordance with other aspects of this particular embodiment, a secondexpected performance of the contact center system may be determinedusing a FIFO pairing strategy, and an expected gain of the contactcenter system may be determined using the pairing model instead of theFIFO pairing strategy.

In accordance with other aspects of this particular embodiment, a secondpairing model may be constructed based at least on a second ordering ofa second plurality of contact types different from the first pluralityof contact types, a second expected performance of the contact centersystem may be determined using the second pairing model, the secondexpected performance based on the second pairing model may be comparedwith the expected performance based on the pairing model, and one of atleast the pairing model and the second pairing model may be selectedbased on the comparing of the expected performance and the secondexpected performance.

In accordance with other aspects of this particular embodiment, newcontact-agent outcome data may be determined, the pairing model may beupdated based on the new contact-agent outcome data, and an updatedexpected performance of the contact center system may be determinedusing the updated pairing model.

In another particular embodiment, the techniques may be realized as asystem for behavioral pairing model evaluation in a contact centersystem comprising at least one computer processor configured to operatein the contact center system, wherein the at least one computerprocessor is configured to perform the steps in the above-discussedmethod.

In another particular embodiment, the techniques may be realized as anarticle of manufacture for behavioral pairing model evaluation in acontact center system comprising a non-transitory processor readablemedium and instructions stored on the medium, wherein the instructionsare configured to be readable from the medium by at least one computerprocessor configured to operate in the contact center system and therebycause the at least one computer processor to operate to perform thesteps in the above-discussed method.

The present disclosure will now be described in more detail withreference to particular embodiments thereof as shown in the accompanyingdrawings. While the present disclosure is described below with referenceto particular embodiments, it should be understood that the presentdisclosure is not limited thereto. Those of ordinary skill in the arthaving access to the teachings herein will recognize additionalimplementations, modifications, and embodiments, as well as other fieldsof use, which are within the scope of the present disclosure asdescribed herein, and with respect to which the present disclosure maybe of significant utility.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to facilitate a fuller understanding of the present disclosure,reference is now made to the accompanying drawings, in which likeelements are referenced with like numerals. These drawings should not beconstrued as limiting the present disclosure, but are intended to beillustrative only.

FIG. 1 shows a block diagram of a contact center according toembodiments of the present disclosure.

FIG. 2 depicts a schematic representation of a BP model according toembodiments of the present disclosure.

FIG. 3 depicts a schematic representation of a BP model according toembodiments of the present disclosure.

FIG. 4 shows a flow diagram of a BP model evaluation method according toembodiments of the present disclosure.

DETAILED DESCRIPTION

A typical contact center algorithmically assigns contacts arriving atthe contact center to agents available to handle those contacts. Attimes, the contact center may have agents available and waiting forassignment to inbound or outbound contacts (e.g., telephone calls,Internet chat sessions, email) or outbound contacts. At other times, thecontact center may have contacts waiting in one or more queues for anagent to become available for assignment.

In some typical contact centers, contacts are assigned to agents orderedbased on time of arrival, and agents receive contacts ordered based onthe time when those agents became available. This strategy may bereferred to as a “first-in, first-out”, “FIFO”, or “round-robin”strategy. In other typical contact centers, other strategies may beused, such as “performance-based routing”, or a “PBR” strategy.

In other, more advanced contact centers, contacts are paired with agentsusing a “behavioral pairing”, or a “BP” strategy, under which contactsand agents may be deliberately (preferentially) paired in a fashion thatenables the assignment of subsequent contact-agent pairs such that whenthe benefits of all the assignments under a BP strategy are totaled theymay exceed those of FIFO and other strategies such as performance-basedrouting (“PBR”) strategies. BP is designed to encourage balancedutilization of agents within a skill queue while neverthelesssimultaneously improving overall contact center performance beyond whatFIFO or PBR methods will allow. This is a remarkable achievementinasmuch as BP acts on the same calls and same agents as FIFO or PBRmethods, utilizes agents approximately evenly as FIFO provides, and yetimproves overall contact center performance. BP is described in, e.g.,U.S. Pat. No. 9,300,802, which is incorporated by reference herein.Additional information about these and other features regarding thepairing or matching modules (sometimes also referred to as “SATMAP”,“routing system”, “routing engine”, etc.) is described in, for example,U.S. Pat. No. 8,879,715, which is incorporated herein by reference.

In some embodiments, a contact center may switch (or “cycle”)periodically among at least two different pairing strategies (e.g.,between FIFO and a BP strategy). Additionally, the outcome of eachcontact-agent interaction may be recorded along with an identificationof which pairing strategy (e.g., FIFO, PBR, or BP) had been used toassign that particular contact-agent pair. By tracking whichinteractions produced which results, the contact center may measure theperformance attributable to a first strategy (e.g., FIFO) and theperformance attributable to a second strategy (e.g., BP). In this way,the relative performance of one strategy may be benchmarked against theother. The contact center may, over many periods of switching betweendifferent pairing strategies, more reliably attribute performance gainto one strategy or the other. Benchmarking pairing strategies isdescribed in, e.g., U.S. patent application Ser. No. 15/131,915, filedApr. 18, 2016, which is incorporated herein by reference.

A BP strategy may develop a model of agents or agent groups and contacttypes, from which expected gain over other pairing strategies may bedetermined. Therefore, techniques for improved model generation andvalidation are desirable to optimize the expected gain.

In view of the foregoing, it may be understood that there is a need fora system that enables improving behavioral pairing model selection toimprove the efficiency and performance of pairing strategies that aredesigned to choose among multiple possible pairings.

FIG. 1 shows a block diagram of a contact center system 100 according toembodiments of the present disclosure. The description herein describesnetwork elements, computers, and/or components of a system and methodfor simulating contact center systems that may include one or moremodules. As used herein, the term “module” may be understood to refer tocomputing software, firmware, hardware, and/or various combinationsthereof. Modules, however, are not to be interpreted as software whichis not implemented on hardware, firmware, or recorded on a processorreadable recordable storage medium (i.e., modules are not software perse). It is noted that the modules are exemplary. The modules may becombined, integrated, separated, and/or duplicated to support variousapplications. Also, a function described herein as being performed at aparticular module may be performed at one or more other modules and/orby one or more other devices instead of or in addition to the functionperformed at the particular module. Further, the modules may beimplemented across multiple devices and/or other components local orremote to one another. Additionally, the modules may be moved from onedevice and added to another device, and/or may be included in bothdevices.

As shown in FIG. 1, the contact center system 100 may include a centralswitch 110. The central switch 110 may receive incoming contacts (e.g.,callers) or support outbound connections to contacts via atelecommunications network (not shown). The central switch 110 mayinclude contact routing hardware and software for helping to routecontacts among one or more contact centers, or to one or more PBX/ACDsor other queuing or switching components, including otherInternet-based, cloud-based, or otherwise networked contact-agenthardware or software-based contact center solutions.

The central switch 110 may not be necessary such as if there is only onecontact center, or if there is only one PBX/ACD routing component, inthe contact center system 100. If more than one contact center is partof the contact center system 100, each contact center may include atleast one contact center switch (e.g., contact center switches 120A and120B). The contact center switches 120A and 120B may be communicativelycoupled to the central switch 110. In embodiments, various topologies ofrouting and network components may be configured to implement thecontact center system.

Each contact center switch for each contact center may becommunicatively coupled to a plurality (or “pool”) of agents. Eachcontact center switch may support a certain number of agents (or“seats”) to be logged in at one time. At any given time, a logged-inagent may be available and waiting to be connected to a contact, or thelogged-in agent may be unavailable for any of a number of reasons, suchas being connected to another contact, performing certain post-callfunctions such as logging information about the call, or taking a break.

In the example of FIG. 1, the central switch 110 routes contacts to oneof two contact centers via contact center switch 120A and contact centerswitch 120B, respectively. Each of the contact center switches 120A and120B are shown with two agents each. Agents 130A and 130B may be loggedinto contact center switch 120A, and agents 130C and 130D may be loggedinto contact center switch 120B.

The contact center system 100 may also be communicatively coupled to anintegrated service from, for example, a third party vendor. In theexample of FIG. 1, pairing model evaluation module 140 may becommunicatively coupled to one or more switches in the switch system ofthe contact center system 100, such as central switch 110, contactcenter switch 120A, or contact center switch 120B. In some embodiments,switches of the contact center system 100 may be communicatively coupledto multiple pairing model evaluation modules (e.g., a BP modelevaluation module). In some embodiments, pairing model evaluation module140 may be embedded within a component of a contact center system (e.g.,embedded in or otherwise integrated with a switch). The pairing modelevaluation module 140 may receive information from a switch (e.g.,contact center switch 120A) about agents logged into the switch (e.g.,agents 130A and 130B) and about incoming contacts via another switch(e.g., central switch 110) or, in some embodiments, from a network(e.g., the Internet or a telecommunications network) (not shown).

A contact center may include multiple pairing modules (e.g., a BP moduleand a FIFO module) (not shown), and one or more pairing modules may beprovided by one or more different vendors. In some embodiments, one ormore pairing modules may be components of pairing model evaluationmodule 140 or one or more switches such as central switch 110 or contactcenter switches 120A and 120B. In some embodiments, a pairing modelevaluation module may determine which pairing module may handle pairingfor a particular contact. For example, the pairing model evaluationmodule may alternate between enabling pairing via the BP module andenabling pairing with the FIFO module. In other embodiments, one pairingmodule (e.g., the BP module) may be configured to emulate other pairingstrategies. For example, a pairing model evaluation module, or a pairingmodel evaluation component integrated with BP components in the BPmodule, may determine whether the BP module may use BP pairing oremulated FIFO pairing for a particular contact. In this case, “BP on”may refer to times when the BP module is applying the BP pairingstrategy, and “BP off” may refer to other times when the BP module isapplying a different pairing strategy (e.g., FIFO).

In some embodiments, regardless of whether pairing strategies arehandled by separate modules, or if some pairing strategies are emulatedwithin a single pairing module, the single pairing module may beconfigured to monitor and store information about pairings made underany or all pairing strategies. For example, a BP module may observe andrecord data about FIFO pairings made by a FIFO module, or the BP modulemay observe and record data about emulated FIFO pairings made by a BPmodule operating in FIFO emulation mode.

FIG. 2 depicts a schematic representation of a BP model 200 according toembodiments of the present disclosure. BP model 200 is a simple 2×2model with an ordering of two groups of agents (Agent Group A and AgentGroup B) and an ordering of two types of contacts (Contact Type A andContact Type B). In real-world contact centers, there may be dozens,hundreds, or more agents or groups ordered in a model, and there mayalso be many more types of contacts ordered in a model.

In BP model 200, there are four pairwise possibilities: a contact ofContact Type A is paired with an agent of Agent Group A (pairing 201); acontact of Contact Type A is paired with an agent of Agent Group B(pairing 202); a contact of Contact Type B is paired with an agent ofAgent Group A (pairing 203); and a contact of Contact Type B is pairedwith an agent of Agent Group B (pairing 204).

In the hypothetical of BP model 200, a review of historical contactoutcome data shows the following: pairing 201 shows a volume of 10contacts and an average revenue of $5 per contact; pairing 202 shows avolume of 10 contacts and an average revenue of $25 per contact; pairing203 shows a volume of 25 contacts and an average revenue of $10 percontact; and pairing 204 shows a volume of 40 contacts and an averagerevenue of $20 per contact.

One technique for computing the expected average revenue per contactacross all contact-agent pairings is to compute the weighted averageacross all possible pairings as shown below in Equation 1:

(25·10+40·20+25·10+10·5)/(25+40+25+10)=13.5  (Eq. 1)

Thus, the expected revenue per contact in a typical FIFO pairing is$13.50 per contact.

In some embodiments of a BP strategy, the contact center system (via,e.g., a BP component or module embedded or communicatively coupled tothe contact center system) may preferably pair contacts to agents alonga diagonal of the model (e.g., diagonal 210). In the example of BP model200, Contact Type A may be preferably paired to Agent Group A, andContact Type B may be preferably paired to Agent Group B given optimalavailability of choice.

One technique for estimating the expected revenue per contact under a BPstrategy is to compute the weighted average across all preferredpairings as shown below in Equation 2:

(40·20+10·5)/(40+10)=17  (Eq. 2)

Thus, the expected revenue per contact appears to be $17 per contact,and the expected gain or improvement over a FIFO pairing strategy is$3.50 per contact, or a gain of almost 26% over FIFO.

The computation shown in Eq. 2 implicitly assumes that calls pairedusing a BP strategy will be distributed uniformly throughout thepairings 201 and 204, and it computes the weighted proportion ofpairings falling uniformly in a square grid with area 10 of pairing 201to the proportion of pairings falling uniformly in a square grid of area40 of pairing 204.

However, in practice, the BP strategy is likely to hew more closely tothe diagonal with respect to the distance or Z-score that a particularpairing of an ordered contact and an ordered agent will fall from thediagonal. A more accurate estimate of expected gain for BP over FIFO mayaccount for a narrower set of contact-agent pairings along the diagonal,computing instead the proportion of pairings falling uniformly along ornear the diagonal through each pairing. In some embodiments, a weightedaverage according to the proportional length of the diagonal througheach preferred pairing under a BP strategy may be computed. Thus, insome embodiments, the adjusted expected revenue per contact is only $15per contact, and the adjusted expected gain or improvement over a FIFOpairing strategy is only $1.50 per contact, or a gain of about 11%.

In practice, the real-world gain measured using BP model 200 is morelikely to be 11% than 26%. Consequently, it may be advantageous todetermine the “adjusted diagonal” rather than the “unadjusted diagonal”of Equation 2. The ability to evaluate expected values and gain of BPmodels has many benefits, including more accurate revenue/cost-savingforecasting, and improved ability to select optimal models. For example,given a choice between two possible models of ordering contact types andagents (e.g., “Model A” and “Model B”), Model A could show a higher gainthan Model B using the unadjusted diagonal computation, whereas Model Bcould show a higher gain than Model A using the adjusted diagonalcomputation. In this case, it would be preferable to apply Model B forBP in the contact center system to maximize the real-world expectedgain.

In some situations, a model might appear to have a positive expectedgain using the unadjusted diagonal computation, but will actually have anegative expected gain (i.e., a loss) using the adjusted diagonalcomputation. An example of such a model is described below withreference to FIG. 3.

FIG. 3 depicts a schematic representation of a BP model 300 according toembodiments of the present disclosure. Similar to BP model 200 (FIG. 2),BP model 300 is a simple, hypothetical 2×2 model with an ordering of twogroups of agents (Agent Group A and Agent Group B) and an ordering oftwo types of contacts (Contact Type A and Contact Type B).

In BP model 300, there are, again, four pairwise possibilities: acontact of Contact Type A is paired with an agent of Agent Group A(pairing 301); a contact of Contact Type A is paired with an agent ofAgent Group B (pairing 302); a contact of Contact Type B is paired withan agent of Agent Group A (pairing 303); and a contact of Contact Type Bis paired with an agent of Agent Group B (pairing 304).

In some embodiments of a BP strategy, the contact center system (via,e.g., a BP component or module embedded or communicatively coupled tothe contact center system) may preferably pair contacts to agents alonga diagonal of the model (e.g., diagonal 310). In the example of BP model300, Contact Type A may be preferably paired to Agent Group A, andContact Type B may be preferably paired to Agent Group B given optimalavailability of choice.

In the hypothetical of BP model 300, a review of historical contactoutcome data shows the following: pairing 301 shows a volume of 21,000contacts and an average handle time (“AHT”) of 900 seconds per contact;pairing 302 shows a volume of 23,000 contacts and an AHT of 850 secondsper contact; pairing 303 shows a volume of 25,000 contacts and an AHT of700 seconds per contact; and pairing 304 shows a volume of 26,000contacts and an average revenue of 650 seconds per contact. Notably, aneffective behavioral pairing model for AHT should result in a reductionin AHT (i.e., a lower expected AHT indicates a positive expected gain).

Equations 3 and 4 below compute the baseline FIFO/random expectedperformance, the unadjusted BP expected performance, and the adjusteddiagonal BP expected performance respectively:

(21,000·900+23,000·850+25,000·700+26,000·650)/(21,000+23,000+25,000+26,000)≈767  (Eq.3)

(21,000·900+26,000·650)/(21,000+26,000)≈762  (Eq. 4)

Thus, the apparent, unadjusted expected performance of BP model 300 isapproximately a 5 second per contact reduction in AHT (767 seconds percontact from Equation 3 less 762 seconds per contact from Equation 4),or a 0.7% gain over FIFO pairing. However, the real-world, adjustedexpected performance of BP model 300 according to some embodiments isapproximately a 1 second per contact increase in AHT (767 seconds percontact from Equation 3 less 768 seconds per contact from an adjusteddiagonal computation), or a −0.1% gain compared to FIFO pairing.

In the example of hypothetical BP model 300, a contact center system mayhave naively selected BP model 300 as a viable model to obtain a 0.7%gain. However, based on the adjustment, the contact center system mayavoid using BP model 300 because it is expected to decrease overallperformance of the contact center system.

FIG. 4 shows a flow diagram of a BP model evaluation method 400according to embodiments of the present disclosure. At block 410, the BPmodel evaluation method 400 may begin.

At block 410, an ordering of agents (or groups of agents) may bedetermined, and, at block 420, an ordering of contact types may bedetermined. A BP module or similar component may assist with definingcontact types and/or agent groups based on a variety of variables (e.g.,demographic, psychographic). Orderings of contact types and agents oragent groups may be based on a variety of performance metrics or othermetrics (sales, AHT, influencability, etc.).

At block 430, historical contact-agent outcome data may be analyzedaccording to the agent and contact type orderings determined at blocks410 and 420. For example, consider a historical pairing between AgentAlice and Contact Bob. An analysis of agent data for Agent Alicedetermines that Agent Alice would be considered a member of Agent GroupA under the BP model determined at blocks 410 and 420, and an analysisof contact data for Contact Bob determines that Contact Bob would beconsidered a member of Contact Type B under the BP model. The relevantoutcome of this contact-agent pairing would be credited to the pairwisepairing group of Agent Group A and Contact Type B. For example, if thecontact center seeks to optimize sales, the pairing model evaluationmodule may note that one of the contacts in this pairing group resultedin a certain amount of revenue (e.g., $0, $10, $100).

The historical outcome data may include a small volume of outcome data,a large volume of outcome data, a threshold amount of outcome datadetermined to be statistically significant, etc. In some embodiments,outcome data may be limited to a rolling historical window (e.g., 10days, 30 days, 90 days, 1 year, etc.). In some embodiments, outcome datamay be limited to outcomes collected during “off” cycles when FIFOpairing may be in use. In other embodiments, outcome data may includerandom groupings of “on” (e.g., BP) and/or “off” (e.g., FIFO) pairings.

After the historical outcome data has been analyzed, the resulting BPmodel may be similar to BP model 200 (FIG. 2) or BP model 300 (FIG. 3),insofar as a grid of pairwise pairing groups for ordered agents or agentgroups and contact types may indicate a contact volume and averageoutcome for each pairing group.

At block 440, a BP correction factor may be applied to the model. Forexample, as shown with respect to BP models 200 and 300 (FIGS. 2 and 3),a behavioral pairing adjustment may be applied to compute the weightedaverage of contacts along proportional lengths of the diagonal throughBP-preferred pairings instead of the weighted proportion of contactswithin the areas of the BP-preferred pairings. For other forms of BP,other comparable techniques may be applied to adjust gain computationsto real-world expectations.

At block 450, an expected gain for the model may be determined based onthe BP correction factor applied at block 440. In this way, a model maybe favored or discarded over other models to optimize the expected gainover the off cycle pairing strategy (e.g., FIFO, PBR).

After block 450, the BP model evaluation method 400 may end. In someembodiments, the BP model evaluation method 400 may return to block 410to begin determining alternative BP models to evaluate and compare toother possible BP models, seeking a model with higher or optimal gain.

At this point it should be noted that behavioral pairing modelevaluation in a contact center system in accordance with the presentdisclosure as described above may involve the processing of input dataand the generation of output data to some extent. This input dataprocessing and output data generation may be implemented in hardware orsoftware. For example, specific electronic components may be employed ina behavioral pairing model evaluation module or similar or relatedcircuitry for implementing the functions associated with pairing modelevaluation in a contact center system in accordance with the presentdisclosure as described above. Alternatively, one or more processorsoperating in accordance with instructions may implement the functionsassociated with BP in a contact center system in accordance with thepresent disclosure as described above. If such is the case, it is withinthe scope of the present disclosure that such instructions may be storedon one or more non-transitory processor readable storage media (e.g., amagnetic disk or other storage medium), or transmitted to one or moreprocessors via one or more signals embodied in one or more carrierwaves.

The present disclosure is not to be limited in scope by the specificembodiments described herein. Indeed, other various embodiments of andmodifications to the present disclosure, in addition to those describedherein, will be apparent to those of ordinary skill in the art from theforegoing description and accompanying drawings. Thus, such otherembodiments and modifications are intended to fall within the scope ofthe present disclosure. Further, although the present disclosure hasbeen described herein in the context of at least one particularimplementation in at least one particular environment for at least oneparticular purpose, those of ordinary skill in the art will recognizethat its usefulness is not limited thereto and that the presentdisclosure may be beneficially implemented in any number of environmentsfor any number of purposes. Accordingly, the claims set forth belowshould be construed in view of the full breadth and spirit of thepresent disclosure as described herein.

1. A method for behavioral pairing model evaluation in a contact centersystem comprising: determining, by at least one computer processorcommunicatively coupled to and configured to operate in the contactcenter system, an ordering of a plurality of agents; determining, by theat least one computer processor, an ordering of a plurality of contacttypes; analyzing, by the at least one computer processor, historicalcontact-agent outcome data according to the orderings of the pluralitiesof agents and contact types to construct a first pairing model;determining, by the at least one computer processor, a first expectedperformance of the contact center system using the first pairing modelconstructing, by the at least one computer processor, a second pairingmodel based at least on a second ordering of a second plurality ofcontact types different from the first plurality of contact types;determining, by the at least one computer processor, a second expectedperformance of the contact center system using the second pairing model;comparing, by the at least one computer processor, the second expectedperformance based on the second pairing model with the first expectedperformance based on the first pairing model; and selecting, by the atleast one computer processor, one of at least the first pairing modeland the second pairing model based on the comparing of the firstexpected performance and the second expected performance.
 2. The methodof claim 1, further comprising: applying, by the at least one computerprocessor and prior to determining the first expected performance, abehavioral pairing correction factor to the first pairing model.
 3. Themethod of claim 1, wherein the first pairing model is a behavioralpairing model.
 4. The method of claim 1, wherein the first pairing modelis based on a diagonal pairing strategy.
 5. The method of claim 1,further comprising: determining, by the at least one computer processor,a third expected performance of the contact center system using a FIFOpairing strategy; and determining, by the at least one computerprocessor, an expected gain of the contact center system using the firstpairing model instead of the FIFO pairing strategy.
 6. The method ofclaim 1, further comprising: selecting, according to the first pairingmodel, by the at least one computer processor, at least oneagent-contact type pairing for connection in the contact center systemto optimize performance of the contact center system attributable to thefirst pairing model.
 7. The method of claim 1, further comprising:determining, by the at least one computer processor, new contact-agentoutcome data; updating, by the at least one computer processor, thefirst pairing model based on the new contact-agent outcome data; anddetermining, by the at least one computer processor, a third expectedperformance of the contact center system using the updated first pairingmodel.
 8. A system for behavioral pairing model evaluation in a contactcenter system comprising: at least one computer processorcommunicatively coupled to and configured to operate in the contactcenter system, wherein the at least one computer processor is furtherconfigured to: determine an ordering of a plurality of agents; determinean ordering of a plurality of contact types; analyze historicalcontact-agent outcome data according to the orderings of the pluralitiesof agents and contact types to construct a first pairing model;determine a first first expected performance of the contact centersystem using the first pairing model construct a second pairing modelbased at least on a second ordering of a second plurality of contacttypes different from the first plurality of contact types; determine asecond expected performance of the contact center system using thesecond pairing model; compare the second expected performance based onthe second pairing model with the first expected performance based onthe first pairing model; and select one of at least the first pairingmodel and the second pairing model based on the comparing of the firstexpected performance and the second expected performance.
 9. The systemof claim 8, wherein the at least one computer processor is furtherconfigured to: apply, prior to determining the first expectedperformance, a behavioral pairing correction factor to the first pairingmodel.
 10. The system of claim 8, wherein the first pairing model is abehavioral pairing model.
 11. The system of claim 8, wherein the firstpairing model is based on a diagonal pairing strategy.
 12. The system ofclaim 8, wherein the at least one computer processor is furtherconfigured to: determine a third expected performance of the contactcenter system using a FIFO pairing strategy; and determine an expectedgain of the contact center system using the first pairing model insteadof the FIFO pairing strategy.
 13. The system of claim 8, wherein the atleast one computer processor is further configured to: select, accordingto the first pairing model, at least one agent-contact type pairing forconnection in the contact center system to optimize performance of thecontact center system attributable to the first pairing model.
 14. Thesystem of claim 8, wherein the at least one computer processor isfurther configured to: determine new contact-agent outcome data; updatethe first pairing model based on the new contact-agent outcome data; anddetermine a third expected performance of the contact center systemusing the updated first pairing model.
 15. An article of manufacture forbehavioral pairing model evaluation in a contact center systemcomprising: a non-transitory processor readable medium; and instructionsstored on the medium; wherein the instructions are configured to bereadable from the medium by at least one computer processorcommunicatively coupled to and configured to operate in the contactcenter system and thereby cause the at least one computer processor tofurther operate so as to: determine an ordering of a plurality ofagents; determine an ordering of a plurality of contact types; analyzehistorical contact-agent outcome data according to the orderings of thepluralities of agents and contact types to construct a first pairingmodel; determine a first expected performance of the contact centersystem using the first pairing model; construct a second pairing modelbased at least on a second ordering of a second plurality of contacttypes different from the first plurality of contact types; determine asecond expected performance of the contact center system using thesecond pairing model; compare the second expected performance based onthe second pairing model with the first expected performance based onthe first pairing model; and select one of at least the first pairingmodel and the second pairing model based on the comparing of the firstexpected performance and the second expected performance.
 16. Thearticle of manufacture of claim 15, wherein the at least one computerprocessor is further caused to operate so as to: apply, prior todetermining the first expected performance, a behavioral pairingcorrection factor to the first pairing model.
 17. The article ofmanufacture of claim 15, wherein the first pairing model is a behavioralpairing model.
 18. The article of manufacture of claim 15, wherein thefirst pairing model is based on a diagonal pairing strategy.
 19. Thearticle of manufacture of claim 15, wherein the at least one computerprocessor is further caused to operate so as to: determine a thirdexpected performance of the contact center system using a FIFO pairingstrategy; and determine an expected gain of the contact center systemusing the first pairing model instead of the FIFO pairing strategy. 20.The article of manufacture of claim 15, wherein the at least onecomputer processor is further caused to operate so as to: select,according to the first pairing model, at least one agent-contact typepairing for connection in the contact center system to optimizeperformance of the contact center system attributable to the firstpairing model.
 21. The article of manufacture of claim 15, wherein theat least one computer processor is further caused to operate so as to:determine new contact-agent outcome data; update the first pairing modelbased on the new contact-agent outcome data; and determine a thirdexpected performance of the contact center system using the updatedfirst pairing model.